ObjectiveBased on traditional quality evaluation of Gardeniae Fructus（GF） recorded in historical materia medica， this study systematically compared the quality differences between wild and cultivated GF from morphological characteristics， microscopic features， and contents of primary and secondary metabolites. MethodsVernier calipers and analytical balances were used to measure the length， diameter and individual fruit weight of wild and cultivated GF， and the aspect ratio was calculated. A colorimeter was used to determine the chromaticity value of wild and cultivated GF， and the paraffin sections of them were prepared by safranin-fast green staining and examined under an optical microscope to observe their microstructure. Subsequently， the contents of water-soluble and alcohol-soluble extracts of wild and cultivated GF were detected by hot immersion method under the general rule 2201 in volume Ⅳ of the 2020 edition of the Pharmacopoeia of the People's Republic of China， the starch content was measured by anthrone colorimetric method， the content of total polysaccharides was determined by phenol-sulfuric acid colorimetric method， the sucrose content was determined by high performance liquid chromatography coupled with evaporative light scattering detection（HPLC-ELSD）， and the contents of representative components in them were measured by ultra-performance liquid chromatography（UPLC）. Finally， correlation analysis was conducted between quality traits and phenotypic traits， combined with multivariate statistical analysis methods such as principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， key differential components between wild and cultivated GF were screened. ResultsIn terms of traits， the wild GF fruits were smaller， exhibiting reddish yellow or brownish red hues with significant variation between batches. While the cultivated GF fruits are larger， displaying deeper orange-red or brownish red. The diameter and individual fruit weight of cultivated GF were significantly greater than those of wild GF， while the blue-yellow value（b^*） of wild GF was significantly higher than that of cultivated GF. In the microstructure， the mesocarp of wild GF contained numerous scattered calcium oxalate cluster crystals， while the endocarp contained stone cell class round， polygonal or tangential prolongation， undeveloped seeds were visible within the fruit. In contrast， the mesocarp of cultivated GF contained few calcium oxalate cluster crystals， or some batches exhibited extremely numerous cluster crystals. The stone cells in the endocarp were predominantly round-like， with the innermost layer arranged in a grid pattern. Seeds were basically mature， and only a few immature seeds existed in some batches. Regarding primary metabolite content， wild GF exhibited significantly higher total polysaccharide level than cultivated GF（P<0.01）. In category-specific component content， wild GF exhibited significantly higher levels of total flavonoids and total polyphenols compared to cultivated GF（P<0.01）. Analysis of 12 secondary metabolites revealed that wild GF exhibited significantly higher levels of Shanzhiside， deacetyl asperulosidic acid methyl ester， gardenoside and chlorogenic acid compared to cultivated GF（P<0.01）. Conversely， the contents of genipin 1-gentiobioside， geniposide and genipin were significantly lower in wild GF（P<0.01）. ConclusionThere are significant differences between wild and cultivated GF in terms of traits， microstructure， and contents of primary and secondary metabolites. At present， the quality evaluation system of cultivated GF remains incomplete， and this study provides a reference for guiding the production of high-quality GF medicinal materials.

ObjectiveTo investigate the mechanism of Yangxin Tongmai Formula （养心通脉方， YTF） in trea-ting coronary heart disease with blood stasis syndrome based on DNA methylation. MethodsSeventy-two SD rats were randomly divided into a control group （n=12） and a modeling group （n=60）. The modeling group was subjected to a high-fat diet， intragastric administration of vitamin D3， and subcutaneous injection of isoprenaline to establish the rat model of coronary heart disease with blood stasis syndrome. Forty-one successfully modeled rats were then randomly allocated into model group， YTF low-， medium-， and high-dose groups， and the atorvastatin calcium group， with 8 rats in each group and 1 rat reserved. The YTF low-， medium-， and high-dose groups received YTF at 6， 12， and 18 g/（kg·d） by gavage， respectively. The atorvastatin calcium group received atorvastatin calcium tablets at 1.8 mg/（kg·d） by gavage. The control group and the model group received 0.9% sodium chloride injection at 4 ml/（kg·d） by gavage. All administrations were performed once daily for 3 weeks. Twenty-four hours after the last administration， serum lipid levels including total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C）， and high-density lipoprotein cholesterol （HDL-C）， myocardial enzymes including cardiac troponin T （cTnT）， creatine kinase MB （CK-MB）， and lactate dehydrogenase （LDH）， and inflammatory factors including interleukin-1β （IL-1β） and interleukin-10 （IL-10） were detected by ELISA. Pathological changes in myocardial tissue were observed via HE staining. Whole blood DNA methylation sequencing was used to analyze differential methylation gene expression among the control group， model group， and YTF high-dose group. Western Blotting was used to verify the protein levels of the key genes and downstream signaling pathways. ResultsCompared to the control group， the model group showed increased levels of TC， TG， LDL-C， cTnT， CK-MB， LDH， and IL-1β， along with decreased levels of HDL-C and IL-10 （P＜0.05 or P＜0.01）. Compared to the model group， all treatment groups exhibited decreased levels of TC， LDL-C， CK-MB， and LDH， along with increased IL-10 levels. Among these， the high-dose YTF group demonstrated superior efficacy in reducing cTnT levels compared to the other TCM groups （P＜0.05 or P＜0.01）. HE staining indicated that the YTF high-dose group ameliorated myocardial cell swelling， disordered arrangement， pyknosis， and disappearance of nuclei， thereby reducing myocardial cell damage. Whole blood DNA methylation sequencing identified 240 differentially methylated genes shared by the control group， model group， and YTF high-dose group， including 109 hypermethylated and 131 hypomethylated genes； eif2ak3 was identified as a key differentially methylated gene. Compared to the control group， the model group exhibited increased protein levels of eukaryotic translation initiation factor 2 alpha kinase 3 （eIf2ak3）， phosphorylated protein kinase RNA-like endoplasmic reticulum kinase （p-PERK）， activating transcription factor 4 （ATF4）， C/EBP homologous protein （CHOP）， and Bax， along with a decreased level of B-cell lymphoma-2 （Bcl-2） protein （P＜0.05 or P＜0.01）. Compared to the model group， the YTF high-dose group showed decreased protein levels of eIf2ak3， p-PERK， ATF4， CHOP， and Bax， and an increased level of Bcl-2 protein （P＜0.05 or P＜0.01）. ConclusionYTF may regulate key differentially methylated genes such as eIf2ak3 and the PERK/ATF4/CHOP signaling pathway， thereby inhibiting endoplasmic reticulum stress， reducing myocardial cell apoptosis， and exerting therapeutic effects in coronary heart disease blood stasis syndrome.

Nuclear factor-kappa B （NF-κB） is an important intracellular transcription factor widely involved in the processes such as immune response， inflammatory response， cell proliferation， and apoptosis. The abnormal activation of the NF-κB signaling pathway plays a pivotal role in various liver diseases including chronic hepatitis， liver fibrosis， liver cirrhosis， and hepatocellular carcinoma. Extensive studies have shown that inhibiting NF-κB activity may effectively reduce inflammation and fibrosis and improve metabolic disorders. Several natural compounds， such as matrine and salvianolic acid B， have shown the potential in suppressing NF-κB activity， thereby exerting anti-inflammatory， anti-fibrotic， and anti-tumor effects. This article systematically reviews the critical role of the NF-κB signaling pathway in liver diseases and its potential as a therapeutic target， in order to highlight its potential as a therapeutic target for liver diseases and provide new directions for the treatment of liver diseases.

Objective　To explore the parainfluenza virus (PIV) infection in children hospitalized in Hainan between March 2012 and December 2022, and to analyze its epidemiological characteristics. Methods　The samples were obtained from 62 553 kids with respiratory infections who were hospitalized in the Department of Pediatrics of multiple hospitals in various regions of Hainan from March 2012 to December 2022. Indirect immunofluorescence was employed to detect IgM antibodies in serum for nine respiratory pathogens, including PIV, adenovirus, influenza A virus, Legionella pneumophila, respiratory syncytial virus, Mycoplasma pneumoniae, influenza B virus, Coxiella burnetii, and Chlamydia pneumoniae. Epidemiological and clinical data (time, gender, age, season, etc.) of PIV-IgM antibody-positive cases were analyzed in a descriptive study. Results　The total PIV-IgM antibody positive rate of 62 553 respiratory tract infected children was 3.29% (2 015/62 553), with the highest positive rate of 11.01% (385/3 496) in 2017, and the second highest positive rate of 8.37% (351/4 196) in 2016, which were significantly higher than the positive rate of the rest of the years (P<0.001). The PIV positive rate was 3.18% (1 248/39 225) in males and 3.29% (767/23 328) in females, with no statistically significant difference (P>0.05). PIV infection occurred in all age groups, with the highest positive rate in the 6 to <12 years group at 4.50% (357/7 941), followed by the 3 to <6 years group at 4.47% (656/14 689), significantly higher than other age groups (P<0.001). The highest positive rate for PIV was in summer at 4.30% (693/16 093), followed by 3.78% (598/15 804) in spring, and the lowest rate of 2.27% (342/15 065) in winter, with statistically significant differences (P<0.001). Single PIV infection accounted for 63.08% (1 271/2 015), while mixed infections accounted for 36.92% (744/2 015), and the most common co-infection being with Mycoplasma pneumoniae infection at 23.13% (466/2 015). Conclusions　PIV is an important pathogen for children's acute respiratory infections in Hainan Province, exhibiting year-round sporadic occurrence with alternating high and low periods characteristics. PIV infection is to the gender of the child, predominantly affects preschool and school-age children, peaks in spring and summer, and commonly co-infects with Mycoplasma pneumoniae infection.

Bronchial asthma （abbreviated as asthma） is one of the common chronic airway inflammatory diseases in the respiratory system， which is difficult to cure. Airway mucus hypersecretion （AMH） is an important factor leading to acute asthma attacks. Traditional Chinese medicine （TCM） possesses therapeutic advantages characterized by multiple pathways， multiple targets， and multiple links， and its mechanism of action in intervening in AMH has gradually drawn attention. TCM can effectively alleviate the symptoms of patients by intervening in asthma through methods such as eliminating phlegm and eliminating fluid retention. This review finds that the pathogenesis of asthma-associated AMH is correlated with decreased mucociliary clearance function and enhanced mucus secretion function； single TCM （such as Platycodon grandiflorum）， effective components of TCM （such as pinellia polysaccharides）， and compound prescriptions （mainly heat-clearing and phlegm-resolving prescriptions， etc.） can improve asthma-associated AMH by regulating the PI3K/Akt and JAK/STAT signaling pathways， inhibiting airway inflammatory responses， oxidative stress， and recovering the water-salt ratio of the mucus layer itself.

OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.